{"title":"Interval models for uncertainty analysis and degradation prediction of the mechanical properties of rubber","authors":"Shengwen Yin, Yawen Lu, Yu Bai","doi":"10.1515/rams-2023-0142","DOIUrl":null,"url":null,"abstract":"As rubber is a hyperelastic material, its nonlinear deformation behavior during aging is significantly influenced by various factors, including the material characteristics, demonstrating a significant uncertainty. Most of the existing uncertain prediction methods of rubber nonlinear property degradation are based on the probability density function, which requires a large number of samples to obtain the probability distribution and requires a lot of work. Therefore, the interval model is used in this study to characterize the uncertainty. However, the traditional interval constitutive models ignore the correlation between interval variables, and the prediction results have large errors. In order to minimize prediction errors and improve prediction accuracy, an interval Mooney–Rivlin (M–R) correlation model that considers the correlation between parameters was established. To address the influence of uncertainties, an interval Arrhenius model was constructed. The M–R model requires multiple fittings of stress–strain curves to obtain the model parameters, and the prediction process is relatively complex. Therefore, combing the two proposed models, the relationship equations of rubber tensile stress with aging temperature and aging time were first established by interval Arrhenius, and then the interval M–R model was used to obtain the variation ranges of parameters <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_rams-2023-0142_eq_001.png\" /> <m:math xmlns:m=\"http://www.w3.org/1998/Math/MathML\"> <m:msub> <m:mrow> <m:mi>C</m:mi> </m:mrow> <m:mrow> <m:mn>10</m:mn> </m:mrow> </m:msub> </m:math> <jats:tex-math>{C}_{10}</jats:tex-math> </jats:alternatives> </jats:inline-formula> and <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_rams-2023-0142_eq_002.png\" /> <m:math xmlns:m=\"http://www.w3.org/1998/Math/MathML\"> <m:msub> <m:mrow> <m:mi>C</m:mi> </m:mrow> <m:mrow> <m:mn>01</m:mn> </m:mrow> </m:msub> </m:math> <jats:tex-math>{C}_{01}</jats:tex-math> </jats:alternatives> </jats:inline-formula>. By contrasting this with the measured rubber aging information, the effectiveness of the proposed model was confirmed. Compared with the prediction model based on the average value, the maximum error of prediction of this model is reduced by about 60%. Compared with the traditional interval model, the prediction region is significantly reduced, which further improves the prediction accuracy. The above results indicate that this interval aging lifetime prediction model is suitable for characterizing the nonlinear stress–strain behavior of rubber-like elastomers.","PeriodicalId":54484,"journal":{"name":"Reviews on Advanced Materials Science","volume":"2 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reviews on Advanced Materials Science","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1515/rams-2023-0142","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
As rubber is a hyperelastic material, its nonlinear deformation behavior during aging is significantly influenced by various factors, including the material characteristics, demonstrating a significant uncertainty. Most of the existing uncertain prediction methods of rubber nonlinear property degradation are based on the probability density function, which requires a large number of samples to obtain the probability distribution and requires a lot of work. Therefore, the interval model is used in this study to characterize the uncertainty. However, the traditional interval constitutive models ignore the correlation between interval variables, and the prediction results have large errors. In order to minimize prediction errors and improve prediction accuracy, an interval Mooney–Rivlin (M–R) correlation model that considers the correlation between parameters was established. To address the influence of uncertainties, an interval Arrhenius model was constructed. The M–R model requires multiple fittings of stress–strain curves to obtain the model parameters, and the prediction process is relatively complex. Therefore, combing the two proposed models, the relationship equations of rubber tensile stress with aging temperature and aging time were first established by interval Arrhenius, and then the interval M–R model was used to obtain the variation ranges of parameters C10{C}_{10} and C01{C}_{01}. By contrasting this with the measured rubber aging information, the effectiveness of the proposed model was confirmed. Compared with the prediction model based on the average value, the maximum error of prediction of this model is reduced by about 60%. Compared with the traditional interval model, the prediction region is significantly reduced, which further improves the prediction accuracy. The above results indicate that this interval aging lifetime prediction model is suitable for characterizing the nonlinear stress–strain behavior of rubber-like elastomers.
期刊介绍:
Reviews on Advanced Materials Science is a fully peer-reviewed, open access, electronic journal that publishes significant, original and relevant works in the area of theoretical and experimental studies of advanced materials. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-time preservation, language-correction services, no space constraints and immediate publication.
Reviews on Advanced Materials Science is listed inter alia by Clarivate Analytics (formerly Thomson Reuters) - Current Contents/Physical, Chemical, and Earth Sciences (CC/PC&ES), JCR and SCIE. Our standard policy requires each paper to be reviewed by at least two Referees and the peer-review process is single-blind.